Abstract
The suffix tree is a data structure for indexing strings. It is used in a variety of applications such as bioinformatics, time series analysis, clustering, text editing and data com-pression. However, when the string and the resulting suffix tree are too large to fit into the main memory, most existing construction algorithms become very inefficient. This paper presents a disk-based suffix tree construction method, called Elastic Range (ERa), which works efficiently with very long strings that are much larger than the available memory. ERa partitions the tree construction process hor-izontally and vertically and minimizes I/Os by dynamically adjusting the horizontal partitions independently for each vertical partition, based on the evolving shape of the tree and the available memory. Where appropriate, ERa also groups vertical partitions together to amortize the I/O cost. We developed a serial version; a parallel version for shared-memory and shared-disk multi-core systems; and a parallel version for shared-nothing architectures. ERa indexes the entire human genome in 19 minutes on an ordinary desk-top computer. For comparison, the fastest existing method needs 15 minutes using 1024 CPUs on an IBM BlueGene supercomputer.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the VLDB Endowment |
| Pages | 49-60 |
| Number of pages | 12 |
| Volume | 5 |
| Edition | 1 |
| Publication status | Published - Sep 2011 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)
Cite this
ERA : Efficient serial and parallel suffix tree construction for very long strings. / Mansour, Essam; Allam, Amin; Skiadopoulos, Spiros; Kalnis, Panos.
Proceedings of the VLDB Endowment. Vol. 5 1. ed. 2011. p. 49-60.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - ERA
T2 - Efficient serial and parallel suffix tree construction for very long strings
AU - Mansour, Essam
AU - Allam, Amin
AU - Skiadopoulos, Spiros
AU - Kalnis, Panos
PY - 2011/9
Y1 - 2011/9
N2 - The suffix tree is a data structure for indexing strings. It is used in a variety of applications such as bioinformatics, time series analysis, clustering, text editing and data com-pression. However, when the string and the resulting suffix tree are too large to fit into the main memory, most existing construction algorithms become very inefficient. This paper presents a disk-based suffix tree construction method, called Elastic Range (ERa), which works efficiently with very long strings that are much larger than the available memory. ERa partitions the tree construction process hor-izontally and vertically and minimizes I/Os by dynamically adjusting the horizontal partitions independently for each vertical partition, based on the evolving shape of the tree and the available memory. Where appropriate, ERa also groups vertical partitions together to amortize the I/O cost. We developed a serial version; a parallel version for shared-memory and shared-disk multi-core systems; and a parallel version for shared-nothing architectures. ERa indexes the entire human genome in 19 minutes on an ordinary desk-top computer. For comparison, the fastest existing method needs 15 minutes using 1024 CPUs on an IBM BlueGene supercomputer.
AB - The suffix tree is a data structure for indexing strings. It is used in a variety of applications such as bioinformatics, time series analysis, clustering, text editing and data com-pression. However, when the string and the resulting suffix tree are too large to fit into the main memory, most existing construction algorithms become very inefficient. This paper presents a disk-based suffix tree construction method, called Elastic Range (ERa), which works efficiently with very long strings that are much larger than the available memory. ERa partitions the tree construction process hor-izontally and vertically and minimizes I/Os by dynamically adjusting the horizontal partitions independently for each vertical partition, based on the evolving shape of the tree and the available memory. Where appropriate, ERa also groups vertical partitions together to amortize the I/O cost. We developed a serial version; a parallel version for shared-memory and shared-disk multi-core systems; and a parallel version for shared-nothing architectures. ERa indexes the entire human genome in 19 minutes on an ordinary desk-top computer. For comparison, the fastest existing method needs 15 minutes using 1024 CPUs on an IBM BlueGene supercomputer.
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UR - http://www.scopus.com/inward/citedby.url?scp=84863749939&partnerID=8YFLogxK
M3 - Chapter
VL - 5
SP - 49
EP - 60
BT - Proceedings of the VLDB Endowment
ER -